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Syntactic Correspondences Grammatical functions Analyses and constraints

LFG

Syntactic Theory Winter Semester 2009/2010 Antske Fokkens

Department of Computational Linguistics Saarland University

17 November 2009

Antske Fokkens Syntax — Lexical Functional Grammar 1 / 46

Syntactic Correspondences Grammatical functions Analyses and constraints

Outline

1

Syntactic Correspondences

2

Grammatical functions

3

Analyses and constraints

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Syntactic Correspondences Grammatical functions Analyses and constraints

Outline

1

Syntactic Correspondences

2

Grammatical functions

3

Analyses and constraints

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Syntactic Correspondences Grammatical functions Analyses and constraints

Function φ

φ maps nodes to their associated f-structure, i.e. φ: N → F φ(n) leads to the f-structure associated with n φ(M(n)) leads to the f-structure associated with the mother node of n ↓ ≡ φ(n) ↑ ≡ φ(M(n))

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Syntactic Correspondences Grammatical functions Analyses and constraints

Mapping from c- to f-structure: The head convention

Consider the following example:

S φ: N → F NP VP N V David smiled 2 6 6 6 6 6 6 4

PRED

’smile<(↑ SUBJ)>’

TENSE PAST SUBJ

2 6 4

PRED

’David’

NUM SG PERS

3 3 7 5 3 7 7 7 7 7 7 5

The head convention states that a phrase inherits its functional properties and requirements from its head: a constituent structure phrase and its head map to the same f-structure S, VP and V thus map to the same f-structure

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Syntactic Correspondences Grammatical functions Analyses and constraints

Annotating PS-rules: heads

Consider the following rule to expand VP to V

VP → V

We express the fact that VP and V have the same f-structure by annotating the V-node:

VP → V φ(M(n)) = φ(n)

This equation indicates that the f-structure of the mothernode of V (φ(M(n))) is equal to the node of V (φ(n))

An alternative notation: VP → V ↑ = ↓

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Syntactic Correspondences Grammatical functions Analyses and constraints

Annotating PS-rules: grammatical functions

Consider the following example:

S φ: N → F NP VP »

SUBJ

hi –

Here the NP bears the SUBJ function The following phrase structure rule carries the additional information to derive the correct f-structure:

S → NP VP (φ(M(n)) SUBJ)= φ(n) φ(M(n)) = φ(n)

An alternative notation:

S → NP VP (↑ SUBJ) = ↓ ↑ = ↓

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Syntactic Correspondences Grammatical functions Analyses and constraints

Lexical Entries

In lexical entries, information about the item’s f-structure is represented in the same way as in c-structures: smiled V (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST The equivalent phrase structure rule: V → smiled (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

An example analysis: David smiled

We assume the following annotated PS-rules:

S → NP VP (↑ SUBJ) = ↓ ↑ = ↓ VP → V ↑ = ↓ NP → N ↑ = ↓

and the following lexical entries

smiled V (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST David N (↑ PRED) ’David’ (↑ NUMBER) = SG (↑ PERSON) = 3

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Syntactic Correspondences Grammatical functions Analyses and constraints

Analysis of David smiled

S NP (↑ SUBJ) = ↓ N ↑ = ↓ David (↑ PRED) = ’David’ (↑ NUMBER) = SG (↑ PERSON) = 3 VP ↑ = ↓ V ↑ = ↓ smiled (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

Instantiating the f-description of the sentence

In order to get the functional description of the sentence, we associate each node with an f-structure:

S NP (↑ SUBJ) = ↓ N ↑ = ↓ David (↑ PRED) = ’David’ (↑ NUMBER) = SG (↑ PERSON) = 3 VP ↑ = ↓ V ↑ = ↓ smiled (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST

f s corresponds to node S f np corresponds to node NP f n corresponds to node N f vp corresponds to node VP f v corresponds to node V

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Syntactic Correspondences Grammatical functions Analyses and constraints

References of ↑ and ↓

S NP (↑ SUBJ) = ↓ N ↑ = ↓ David (↑ PRED) = ’David’ (↑ NUMBER) = SG (↑ PERSON) = 3 VP ↑ = ↓ V ↑ = ↓ smiled (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

References of ↑ and ↓

S NP (↑ SUBJ) = ↓ N ↑ = ↓ David (f n PRED) = ’David’ (f n NUMBER) = SG (f n PERSON) = 3 VP ↑ = ↓ V ↑ = ↓ smiled (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

References of ↑ and ↓

S NP (↑ SUBJ) = ↓ N f np =fn David (f n PRED) = ’David’ (f n NUMBER) = SG (f n PERSON) = 3 VP ↑ = ↓ V ↑ = ↓ smiled (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

References of ↑ and ↓

S NP (f s SUBJ) = f np N f np =fn David (f n PRED) = ’David’ (f n NUMBER) = SG (f n PERSON) = 3 VP f s = f vp V f vp = f v smiled (f v PRED) = ’smile<(↑ SUBJ)>’ (f v TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

The functional description

The tree on the previous slide provides the following functional description:

(f s SUBJ) = f np f np = f n (f n PRED) = ’David’ (f n NUMBER) = SG (f n PERSON) = 3 f s = f vp f vp = f v (f v PRED) = ’smile<(↑SUBJ)>’ (f v TENSE) = PAST

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Syntactic Correspondences Grammatical functions Analyses and constraints

The functional description

The tree on the previous slide provides the following functional description:

(f s SUBJ) = f np f np = f n (f n PRED) = ’David’ (f n NUMBER) = SG (f n PERSON) = 3 f s = f vp f vp = f v (f v PRED) = ’smile<(↑SUBJ)>’ (f v TENSE) = PAST f s, f vp, f v 2 6 6 6 6 6 6 6 6 4

PRED

’smile<(↑SUBJ)>’

TENSE PAST SUBJ

f np, f n 2 6 6 4

PRED

’David’

NUMBER SG PERSON

3 3 7 7 5 3 7 7 7 7 7 7 7 7 5

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Syntactic Correspondences Grammatical functions Analyses and constraints

F-structure of an utterance

The minimal solution of a functional description is the f-structure that satisfies the functional description and contains no additional attribute-value pairs The f-structure of an utterance is the minimal solution satisfying the constraints introduced by the words and the phrase structure of the utterance (Dalrymple 2001, p: 101) In other words: the f-structure of an utterance must contain all relevant information provided by the string, and nothing more

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Syntactic Correspondences Grammatical functions Analyses and constraints

David smiled: f- and annotated c-structure

S NP (f s SUBJ) = f np N f np = f n David (f n PRED) = ’David’ (f n NUMBER) = SG (f n PERSON) = 3 VP f s = f vp V f vp = f v smiled (f v PRED) = ’smile<(↑ SUBJ)>’ (f v TENSE) = PAST

f s, f vp, f v 2 6 6 6 6 6 6 6 6 4

PRED

’smile<(↑SUBJ)>’

TENSE PAST SUBJ

f np, f n 2 6 6 4

PRED

’David’

NUMBER SG PERSON

3 3 7 7 5 3 7 7 7 7 7 7 7 7 5

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Syntactic Correspondences Grammatical functions Analyses and constraints

Adjuncts

The attribute ADJ takes a set as its value The c-structure/f-structure correspondance rule expresses membership to a set as follows:

N → AdjP N ↓ ∈ (↑ ADJ) ↑ = ↓

N A N ↓ ∈ (↑ ADJ) ↑ = ↓ pretty girl 2 6 6 6 6 6 4

PRED

’girl’

NUMBER SG PERSON

3

ADJ

 h

PRED

’pretty’ i ff 3 7 7 7 7 7 5

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Syntactic Correspondences Grammatical functions Analyses and constraints

Outline

1

Syntactic Correspondences

2

Grammatical functions

3

Analyses and constraints

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Syntactic Correspondences Grammatical functions Analyses and constraints

Grammatical functions in LFG

Recall: LFG has a universal inventory of arguments, which can be cross-classified in several ways: Governable functions: SUBJ, OBJ, XCOMP, COMP, OBJθ,

OBLθ

Modifiers: ADJ, XADJ

Core arguments/terms: SUBJ, OBJ, OBJθ Non-term/oblique functions: OBLθ Semantically unrestricted functions: SUBJ, OBJ Semantically restricted functions: OBJθ, OBLθ

Open functions: XCOMP, XADJ Closed functions: SUBJ, OBJ, COMP, OBJθ, OBLθ, ADJ We have seen governable functions and modifiers, in this lecture we’ll look at other divisions and grammatical functions

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Syntactic Correspondences Grammatical functions Analyses and constraints

Terms and non-terms

Among governable functions, we distinguish terms ’direct functions’ and nonterms ’oblique functions’

Terms: SUBJ, OBJ, OBJθ NON-TERMS: OBLθ, XCOMP, COMP

The phenomena may distinguish terms from nonterms:

Agreement: in some language all and only term nominals trigger verb agreement (Ojibwa) (Southern Tiwa) Anaphoric binding patterns: in some languages terms behave differently with respect to anaphoric binding:

Albanian: terms may be antecedent of any governable grammatical function, obliques may only be antecedent of

• bliques

Word order requirements:

In English, terms precede nonterms

Based on Dalrymple (2001) Antske Fokkens Syntax — Lexical Functional Grammar 19 / 46

Syntactic Correspondences Grammatical functions Analyses and constraints

Semantically restricted and unrestricted functions

Subjects and objects are semantically unrestricted. In

• ther words, the can be associated with any thematic role

(Fillmore 1968), subject examples:

AGENT he hit the ball EXPERIENCER he felt cold THEME he lives in Saarbrücken PATIENT the window broke INSTRUMENT the stone broke the window

OBJθ and OBLθ are bound to a specific thematic role, e.g. OBJTHEME must always be a theme

I gave her a book I asked him a question

Based on Dalrymple (2001) Antske Fokkens Syntax — Lexical Functional Grammar 20 / 46

Syntactic Correspondences Grammatical functions Analyses and constraints

Subject I

The subject is the highest argument in the Keenan-Comrie hierarchy If a phenomenon is only applicable to one grammatical function, this is often the subject There are many tests to identify the subject, which tests apply differs from language to language (as for all functions)

Agreement: the subject is often the argument that agrees with the verb

Moravcsik’s universal: there is no language in which the verbs agrees with an element distinct from the intransitive subject, which does not also include sentences where the verb agrees with the intransitive subject

Honorification: in Japanese honorific verb forms are used to honor the subject (Matsumoto (1996))

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Syntactic Correspondences Grammatical functions Analyses and constraints

Subject II

(1) sensei teacher wa

TOPIC

hon book-ACC

• -yomi

honorific-READ ni

COPULA

narimashi-ta become.POLITE-PAST ’the teacher read a book’ (2) * Jon John wa

TOPIC

sensei teacher ni by

• -tasuke-rare

HONORIFIC-help-PASSIVE

ni

COPULA

nat-ta become-PAST ’John was saved by the teacher’ Subject noncoreference: in Hindi the antecedent of a pronoun cannot be a subject of the same clause (Mohanan (1994))

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Syntactic Correspondences Grammatical functions Analyses and constraints

Subject III

(3) Vijay Vijay ne

ERG

Ravii Ravi ko

ACC

uskii his saikil bicycle par

LOC

bithaayaa sit.CAUSATIVE.PERFECT “Vijayi seated Ravij on his∗i,j bike”

The subject condition:

The subject condition states that: Every verbal predicate must have a subject → no consensus to whether this is universal, or only holds for most languages

Based on Dalrymple (2001) Antske Fokkens Syntax — Lexical Functional Grammar 23 / 46

Syntactic Correspondences Grammatical functions Analyses and constraints

Objects

In some languages, there is a clear distinction between subjects and objects on the one hand, and other functions

• n the other hand

Languages may reveal subject and object agreement on the verb (e.g. Palauan, Abkhaz, Jingulu, Malayam) Languages may allow only subjects and objects to be relativized (e.g. Kinyarwanda)

Case marking can also indicate whether an element is an

• bject, but note that this is seldom a one-to-one mapping

Based on Dalrymple (2001) Antske Fokkens Syntax — Lexical Functional Grammar 24 / 46

Syntactic Correspondences Grammatical functions Analyses and constraints

Multiple objects

In many languages, there may be more than one phrase bearing the object function

e.g. He gave her a book

Originally, these second objects where called ’indirect

• bjects’ IOBJ or OBJ2 (after traditional grammar

approaches) It has been observed though, that languages only have

• ne unrestricted object, the secondary object is usually

thematically restricted

e.g. English: OBJTHEME He made her a cake * He made a cake her

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Syntactic Correspondences Grammatical functions Analyses and constraints

Oblique

Oblique arguments are

associated with a particular semantic role marked to indicate their function overtly

English marks oblique arguments with prepositions, in

• ther languages, cases may be used

Oblique arguments may

1 bear a mark that reflects their semantic role (’semantic

case’),

e.g. OBLGOAL in He gave the book to Chris

2 bear an idiosyncratic marker (’quirky case’) (Butt and King

(1999))

e.g. David relied on/*to/*about Chris

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Syntactic Correspondences Grammatical functions Analyses and constraints

Clausal functions

COMP, XCOMP and XADJ are clausal functions

the X in XCOMP and XADJ indicates that these functions are

• pen functions: they have an external subject

COMP is a closed function: its subject is internal XADJ differs from COMP and XCOMP in that it is a modifier

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Syntactic Correspondences Grammatical functions Analyses and constraints

Clausal functions, examples I

COMP clauses containing an overt subject internal to their

phrase (4) David complained that Chris smiled (5) David wondered who smiled (6) David couldn’t believe how big the house was

XCOMP clauses that do not contain an internal subject,

whose subject must be realized externally (7) David seemed to smile (8) Chris expected David to smile

XADJ a modifier that has a subject that must be specified

externally

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Syntactic Correspondences Grammatical functions Analyses and constraints

Clausal functions, examples II

(9) Stretching his arms, David smiled (10) David announced the news dancing

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Syntactic Correspondences Grammatical functions Analyses and constraints

Outline

1

Syntactic Correspondences

2

Grammatical functions

3

Analyses and constraints

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Syntactic Correspondences Grammatical functions Analyses and constraints

Some more examples of PS-rules

S → NP VP (↑ SUBJ) = ↓ ↑ = ↓ VP → VP ↑ = ↓ PP+ ↓ ∈ (↑ ADJ) ! VP → V ↑ = ↓ NP (↑ OBJ) = ↓ ! 8 > < > : NP (↑ OBJθ) = ↓ ! | | PP (↑ OBJθ) = ↓ ! 9 > = > ; NP → Det ↑ = ↓ AP+ ↓ ∈ (↑ ADJ) ! N ↑ = ↓

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Syntactic Correspondences Grammatical functions Analyses and constraints

LFG Equations

Assigning equations (attribute X has value Y) Constraining equations (attribute X must have value Y) Existential constraints (attribute X must be present and have some value) Negative equations (attribute X may not have value Y)

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Syntactic Correspondences Grammatical functions Analyses and constraints

Assigning equations

Assigning equations assign a specific value to an attribute

e.g. (↑ DEF) = + (↑ TENSE) = PAST

The expression may involve multiple attribute names:

e.g. (↑ SUBJ NUM) = SG (↑ SUBJ PERSON) = 3 The example above can be used to ensure person-number agreement between subject and verb in English

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Syntactic Correspondences Grammatical functions Analyses and constraints

Constraining equations (1/2)

Constraining equation require an attribute to have a specific value

e.g. (↑ VFORM) =c PARTICIPLE The constraint above can be used to ensure that complements of have (for present/past participles) and be (for passives) have the right form. (11) David has smiled/*smile/*smiles (12) The cat was chased/*chase/*chases

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Syntactic Correspondences Grammatical functions Analyses and constraints

Constraining equations (2/2)

They can be introduced in the lexicon or as part of the phrase structure rules:

e.g. S → CP VP (↑ SUBJ COMPFORM) =c THAT This constraint can be used to account for the following data: (13) I believe (that) David smiled. (14) That David smiled surprised me. (15) * David smiled surprised me.

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Syntactic Correspondences Grammatical functions Analyses and constraints

Existential constraints (1/2)

Existential constraints require the presence of a feature

e.g. (↑ DEF) This constraint can be used to account for obligatory presence of determiners for count nouns in singular: girl CN { (↑ NUM) = SG (↑ DEF) | (↑ NUM) = PL } This entry captures: *girl/a girl/the girl/girls

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Syntactic Correspondences Grammatical functions Analyses and constraints

Existential constraints (2/2)

Existential constraints are also found in the argument lists

• f semantic forms

Like assigning and constraining equations, they can be introduced in the phrase structure grammar or in the lexicon:

e.g. S → NP VP (↑ TENSE)

This constraint ensures that a sentence will be headed by a finite verb form. In the examples below smile is not marked for tense: (16) David seemed to smile. (17) * David smile.

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Syntactic Correspondences Grammatical functions Analyses and constraints

Negative equations

Negative equations can be used to negate f-descriptions, i.e. to prohibit them e.g. Quirk et al. (1995) provide the following data:

(i) I know whether/if David smiled (ii) You have to justify whether/*if your journey is really necessary

The following equation prevents if from occurring in sentence (ii) justify (↑ COMP COMPFORM) = IF

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Syntactic Correspondences Grammatical functions Analyses and constraints

Example analysis: the girl seems to walk

C-structure:

S

NP

D the N girl VP V seems VP V to VP V smile

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Syntactic Correspondences Grammatical functions Analyses and constraints

Example analysis: the girl seems to walk

C-structure: + annotations (words are presented in the next slides)

S NP (↑ SUBJ) = ↓ D ↑ = ↓ the N ↑ = ↓ girl VP ↑ = ↓ V ↑ = ↓ seems VP (↑ XCOMP) = ↓ V ↑ = ↓ to VP ↑ = ↓ V ↑ = ↓ smile

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Syntactic Correspondences Grammatical functions Analyses and constraints

Adding lexicon and morphology the girl

The (fully inflected) words the and girl provide the following information the D (↑ DEF) = + girl N (↑ PRED) = ’girl’ (↑ NUMBER) = SG (↑ PERS) = 3 (↑ DEF) Recall that (↑ DEF) as part of the word girl is an existential constraint: definiteness must be specified

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Syntactic Correspondences Grammatical functions Analyses and constraints

The phrase the girl

The NP and its associated f-structure looks like this:

NP D ↑ = ↓ the (↑ DEF)= + N ↑ = ↓ girl (↑ PRED) = ’girl’ (↑ NUM) = SG (↑ PERS) = 3 (↑ DEF)

     

PRED

’girl’

NUM SG PERS

3

DEF

+      

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Syntactic Correspondences Grammatical functions Analyses and constraints

Lexical entry and morphology: seems

seems V ’seem<(↑ XCOMP)>(↑ SUBJ)’ (↑ SUBJ) = (↑ XCOMP SUBJ) (↑ TENSE) = PRESENT (↑ SUBJ PERS) = 3 (↑ SUBJ NUM) = SG (↑ XCOMP VFORM) =c TO-INF Some remarks about these equations:

’seem<(↑ XCOMP)>(↑ SUBJ)’: SUBJ is outside of the square brackets because it is not a semantic argument of seem (↑ SUBJ) = (↑ XCOMP SUBJ) states that the subject of XCOMP and the subject of seem are identical (↑ SUBJ PERS) = 3 and (↑ SUBJ NUM) = SG ensure subject verb agreement (↑ XCOMP TO) =c + constraints the verbform of XCOMP to be an infinitive

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Syntactic Correspondences Grammatical functions Analyses and constraints

Lexical entry and morphology: to smile

to V (↑ TO) = + (↑ VFORM) =c INF smile V (↑ PRED) = ’smile<(↑ SUBJ)>’ (↑ VFORM) = INF Remarks:

The presence of to is provided by the verb selecting XCOMP the verb combined with to has to be an infinitive

How can we prevent an infinitive form such as smile to become the head of a sentence (e.g. *the girl smile)?

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Syntactic Correspondences Grammatical functions Analyses and constraints

LFG – Recap I

LFG (minimally) distinguishes two kinds of representation:

the f-structure encoding functional relation using feature structures the c-structure encoding constituency using phrase structure trees

The function φ allows to map nodes from the c-structure to f-structures (N.B. there is also a relation φ−1 that maps from f-structures to c-structure nodes, which we have not seen in these lectures) Words and structure provide information about grammatical functions in an utterance An f-structure of an utterance is the minimal solution satisfying the constraints provided by the words and structure of the utterance

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Syntactic Correspondences Grammatical functions Analyses and constraints

LFG – Recap II

Equations can be used to constrain what well-formed f-structures may look like. They can:

Assign a specific value to an attribute Restrict attribute to have a specific value Require a feature to be present (with some value) Prevent an attribute from having a specific value

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Syntactic Correspondences Grammatical functions Analyses and constraints

Bibliography I

Bresnan, Joan (2000). Lexical Functional Syntax. Blackwell Publishers: Malden, USA/Oxford UK. Dalrymple, Mary, Ron M. Kaplan, John T. Maxwell III and Annie Zaenen (eds.). (1995) Formal Issues in Lexical-Functional Grammar. CSLI Publications: Palo Alto, USA. Dalrymple, Mary (2001). Lexical Functional Grammar. Academic Press: San Diego, USA/London, UK. Kaplan, Ron (1995). The formal architecture of Lexical-Functional

• Grammar. In: Dalrymple et al. (1995).

Kordoni, Valia (2008a). Syntactic Theory Lectures 5. Course slides. Schneider, Gerold (1998). A Linguistic Comparison of Constituency, Dependency and Link Grammar. Lizentiatsarbeit, Institut für Informatik der Universität Zürich. http://www.ifi.unizh.ch/cl/study/lizarbeiten/lizgerold.pdf.

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